call for presentations / April Workshop on selecting and combining models

[Yoshua Bengio]

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             Call for Presentations: CRM Workshop on 

 Selecting and Combining Models with Machine Learning Algorithms 

                          April 12-14, 2000
		 Centre de Recherches Mathematiques
                             Montreal

Organizers: Yoshua Bengio (Universite de Montreal) and 
            Dale Schuurmans (University of Waterloo)

A central objective of machine learning research is to develop
algorithms that learn predictive relationships from data.  This is a
central component of data mining and knowledge discovery tasks, which
are becoming commonplace applications in the realm of e-commerce.
This is a difficult task, however, because inferring a predictive
function from data is in fact an "ill-posed" problem; that is, many
functions can often "fit" a given finite data set, and yet these
functions might generalize very differently on new data drawn from the
same distribution.

To make this problem well-posed one needs to somehow "calibrate" the
complexity of the proposed function class to the amount and quality of
available sample data.  A classical approach is to perform "model
selection" where one imposes a preference structure over function
classes and then optimizes a combined objective of class preference
and data fit.  In doing so, however, it would be useful to have an
accurate estimate of the expected generalization performance at each
preference level; one could then pick the function class that obtained
the lowest expected error, or combine functions from the functions
classes with the lowest expected error, and so on.  Many approaches
have been proposed in the past for this purpose, both in the
statistics and the machine learning research communities.

Recently in machine learning there has been significant interest in
new techniques for evaluating generalization error, for optimizing
generalization error, and for combining and selecting models.  This is
exemplified, for example, by recent work on Structural Risk
Minimization, Support Vector Machines, various Boosting algorithms,
and the Bagging algorithm.  These new approaches suggest that better
generalization performance can be obtained using new, broadly
applicable procedures.  Progress in this area has not only been
important for improving our understanding of how machine learning
algorithms generalize, it has already been demonstrated to be very
useful for practical applications of machine learning and data
analysis.

This workshop will bring together several key researchers in the
fields of machine learning and statistics to present their recent
results and debate the controversial issues that have been dividing
them in the recent machine learning and neural network conferences.
The following leaders in this field have tentatively accepted to
participate to the workshop as invited speakers:

Peter Bartlett (Australia National University), Leo Breiman
(University of California-Berkeley), Tom Dietterich (Oregon State
University), Yoav Freund (AT&T Labs-Research), Radford Neal
(University of Toronto), Michael Perrone (IBM T.J. Watson Research
Center), Robert Schapire (AT&T Labs-Research), Grace Wahba (University
of Wisconsin at Madison).

The workshop will be sponsored by the CRM (Centre de Recherches
Mathematiques) as well as by the MITACS (Mathematics of Information
Technology And Complex Systems) Network of Centers of Excellence.
 
Contributors to the workshop are invited to submit a short (1 or 2
page) summary in electronic form (ascii text, postscript or pdf) of
the proposed presentation by e-mail, to one of the organizers by
February 1st, 2000: bengioy at iro.umontreal.ca or dale at cs.uwaterloo.ca.

Information on the workshop will be posted on the following web site:
 www.iro.umontreal.ca/~bengioy/crmworkshop2000

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-- 
Yoshua Bengio 
Professeur aggrégé
Département d'Informatique et Recherche Operationnelle
Université de Montréal, 
addresse postale: C.P. 6128 Succ. Centre-Ville, Montreal, Quebec, Canada H3C 3J7
addresse civique: 2920 Chemin de la Tour, Montreal, Quebec, Canada H3T 1J8, #2194
Tel: 514-343-6804. Fax: 514-343-5834. Bureau 3339.
http://www.iro.umontreal.ca/~bengioy
http://www.iro.umontreal.ca/~lisa